IJAMSR - INTERNATIONAL JOURNAL OF ADVANCED MULTIDISCIPLINARY SCIENTIFIC RESEARCH

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Past Issues

Complementing the Linear-Programming Learning Experience with the Design and Use of Computerized Games: The Formula 1 Championship Game

Gerardo L. Febres

CrossRef DOI : 10.31426/ijamsr.2022.5.4.5211

CrossRef DOI URL : https://doi.org/10.31426/ijamsr.2022.5.4.5211

Download PDF Google Search Abstract

This document focuses on modeling complex system situations to achieve an advantage within a competitive context. Our goal is to devise computerized games' characteristics to teach and exercise non-easily quantifiable tasks crucial to the math-modeling process. We split the math-modeling process into three layers and present the games' essential characteristics to fulfill these purposes. We also introduce a computerized game to exercise the math-modeling process and optimization problem formulation. The game is named The Formula 1 Championship, and models of the game were developed in the computerized simulation platform MoNet. It resembles some situations in which team managers must make crucial decisions to enhance their racing cars up to the feasible, most advantageous conditions. This paper describes the game's rules, limitations, and five Formula 1 circuit simulators used for the championship development.

Combination Of Computed Tomography (Ct) Images with A Chest X-Ray Diagnostic System Using Deep Learning

Yuvam Sharma, Prof. Asna Furqan

CrossRef DOI : 10.31426/ijamsr.2022.5.4.5212

CrossRef DOI URL : https://doi.org/10.31426/ijamsr.2022.5.4.5212

Download PDF Google Search Abstract

For the purpose of this project, a chest x-ray diagnostic system will be developed that is completely automated. Predictions based on chest radiographs (x-rays) may be generated by the platform's users and professional diagnostic centres. Clinically, chest X-rays have a significant impact on illness detection. Non-COVID-19 individuals with lung infection may be evaluated using chest X-rays (CXRs) as a first-line triage procedure. CXR pictures of COVID-19 and Lung infections caused by other infections are similar enough to make the differentiation difficult for radiologists. Using machine learning-based classifiers, we predicted that CXR pictures from COVID-19 patients could be consistently differentiated from other types of lung infection. A machine learning classifier that can accurately and sensitively discriminate between COVID-19 and non-COVID-19 instances was developed using a dimensionality reduction approach that generated a collection of optimum features from CXR pictures.

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